CN107257756B

CN107257756B - Techniques for assisting a vehicle in situations of varying road conditions

Info

Publication number: CN107257756B
Application number: CN201680012441.0A
Authority: CN
Inventors: I·塔托里安; R·H·沃海比; H·李
Original assignee: Intel Corp
Current assignee: Intel Corp
Priority date: 2015-03-27
Filing date: 2016-02-26
Publication date: 2020-11-03
Anticipated expiration: 2036-02-26
Also published as: US20170129491A1; US9550495B2; WO2016160206A1; US20160280224A1; JP2020057443A; CN107257756A; KR20170131375A; EP3274956A4; KR102457312B1; JP7425608B2; US20190283753A1; KR20200020016A; KR102571303B1; KR20230098363A; US10737698B2; EP3792864A1; JP2018515823A; CN111439250A; KR102548557B1; US10099697B2

Abstract

Technologies for assisting a vehicle in the event of a road condition change include vehicle assistance data based on crowd-sourced road data received from a plurality of vehicle and/or infrastructure sensors. The crowd-sourced road data may be associated with particular road segments and may be used for various characteristics of the road, such as grade, surface, hazardous conditions, and the like. The vehicle assistance data may be provided to an in-vehicle computing device to assist or facilitate traversal of the roadway.

Description

Techniques for assisting a vehicle in situations of varying road conditions

Cross Reference to Related Applications

This application claims priority from U.S. utility patent application serial No. 14/671,755 entitled "TECHNOLOGIES FOR ASSISTINGVEHICLES WITH CHANGING ROAD CONDITIONS" filed on 27/3/2015.

Background

Many modern vehicles, including luxury vehicles, include one or more in-vehicle computing systems for improving the passenger experience. The on-board computing system may include various sensors for obtaining many different types of information about the vehicle, such as temperature sensors, tire pressure sensors, cameras, radar, lidar, oil pressure sensors, fuel tank sensors, speed sensors, and so forth. Vehicle sensors cooperate to provide information to the driver and allow the in-vehicle computing system to improve the experience of driving the vehicle. However, such information is typically based only on sensor data generated by the sensors of that particular vehicle, which may be limited to past operation of the vehicle.

Drawings

The concepts described herein are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. For simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. Where considered appropriate, reference numerals have been repeated among the figures to indicate corresponding or analogous elements.

FIG. 1 is a simplified block diagram of at least one embodiment of a vehicle assistance system for generating vehicle assistance data;

FIG. 2 is a simplified block diagram of at least one embodiment of a vehicle assistance server of the system of FIG. 1;

FIG. 3 is a simplified block diagram of at least one embodiment of an in-vehicle computing system of the system of FIG. 1;

FIG. 4 is a simplified block diagram of at least one embodiment of an environment that may be established by the vehicle assistance server of FIG. 2;

FIG. 5 is a simplified block diagram of at least one embodiment of an environment that may be established by the in-vehicle computing system of FIG. 3;

FIG. 6 is a simplified flow diagram of at least one embodiment of a method for generating crowd-sourced road data that may be performed by the vehicle assistance server of FIG. 2;

FIG. 7 is a simplified flow diagram of at least one embodiment of a method for generating vehicle assistance data that may be performed by the vehicle assistance server of FIG. 2; and is

FIG. 8 is a simplified flow diagram of at least one embodiment of a method for assisting a driver of a vehicle that may be performed by the in-vehicle computing system of FIG. 3.

Detailed Description

While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.

References in the specification to "one embodiment," "an illustrative embodiment," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. Additionally, it should be appreciated that items included in the list in the form of "at least one A, B and C" may mean (A); (B) (ii) a (C) (ii) a (A and B); (A and C); (B and C); or (A, B and C). Similarly, an item listed in the form of "at least one of A, B or C" can mean (a); (B) (ii) a (C) (ii) a (A and B); (A and C); (B and C); or (A, B and C).

In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disk, or other media device).

In the drawings, some structural or methodical features may be shown in a particular arrangement and/or ordering. However, it is to be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, such features may be arranged in a manner and/or order different from that shown in the illustrative figures. In addition, the inclusion of a structural or methodical feature in a particular figure is not meant to imply that such feature is required in all embodiments, and in some embodiments, such feature may not be included or may be combined with other features.

Referring now to FIG. 1, a method for collecting crowd-sourced road data and assisting a vehicle in situations of varying road conditions is shownIllustrative system 100 of a vehicle. The system 100 includes a vehicle assistance server 102 connected to a road system 106. Road system 106 may include one or more in-vehicle computing systems 110, one or more infrastructure sensors 112, and other sensors 114. The vehicle assistance server 102 is connected to an in-vehicle computing system 110, infrastructure sensors 112, and other sensors 114 through the network 104. Although the illustrative embodiment of FIG. 1 includes two in-vehicle computing systems 110 and two infrastructure sensors 112, it should be understood that any number of in-vehicle communication systems 110 or infrastructure sensors 112 may be connected to the vehicle assistance server 102. The network 104 may be any type of communication network and may be configured for communication using any one or more communication technologies (e.g., wired or wireless communication) and associated protocols (e.g., ethernet, etc.),

WiMAX, etc.) to enable such communication.

Each in-vehicle computing system 110 is associated with a vehicle 108. Vehicle 108 may be embodied as any type of vehicle that may travel along a roadway and may include gasoline-powered vehicles, diesel-powered vehicles, natural gas-powered vehicles, electric vehicles, all-terrain vehicles, motorcycles, and other types of vehicles. In some embodiments, the vehicle 108 may be implemented as a type of vehicle that cannot travel along a road, such as a boat, airplane, train, or drone. The infrastructure sensors 112 may be implemented as any type of sensor associated with the roadway system 106 that is not part of the vehicle 108. The infrastructure sensors 112 may include, for example, traffic cameras, weather sensors, position sensors, speed sensors, and other sensors. Other sensors 114 include any type of sensor used by the vehicle assistance system 100 to acquire road data. For example, other types of sensors 114 may include cameras and other sensors attached to a drone (or other aircraft) for monitoring a road.

In current vehicle systems, there is little cooperation between in-vehicle computing systems 110 traveling on the same road. Thus, many of the systems built into the vehicle are purely reactive to changing road conditions encountered by the vehicle 108, whereas in reality other vehicles may just encounter the same road conditions. For example, when the vehicle 108 operating under the cruise control system begins to climb a hill on the road, the cruise control system will not change the throttle of the vehicle 108 until the vehicle 108 has slowed sufficiently to trigger the cruise control feedback system. These delays between changing road conditions (e.g., the uphill grade of the road) and applying cruise control with more throttle for the vehicle 108 may result in the occupant of the vehicle 108 experiencing jerky motion when the vehicle 108 suddenly decelerates.

In another example, the vehicle 108 may predict a distance until the vehicle 108 will need to be refueled or recharged. The current refueling distance estimation does not take into account the uphill and downhill slopes of the road. The estimated refueling distance may vary greatly as the vehicle 108 traverses hilly terrain. For example, when the vehicle 108 is ascending a hill, the distance-to-empty prediction of the vehicle 108 may report that the vehicle 108 has 10 miles until refueling, but when the vehicle 108 is descending a hill, the distance-to-empty prediction may report that the vehicle 108 has 200 miles until refueling is needed. These changes in the refueling distance prediction data may lead to refueling anxiety for the vehicle operator.

The vehicle assistance system 100 provides a way to collect crowd sourced road data from the road system 106, analyze the road data, and feed vehicle assistance data to individual vehicles to allow the vehicle 108 and the vehicle driver to actively control the vehicle 108 rather than reactively control the vehicle 108. For example, using crowd sourced road data, the vehicle assistance server 102 may be configured to inform the cruise control system of the vehicle 108 that an uphill portion of a road is approaching and apply additional throttle to the vehicle 108 before encountering an uphill grade. In another example, the vehicle assistance server 102 may use the crowd-sourced road data to allow the vehicle 108 to have a more accurate estimation of the depletion distance by alerting the vehicle 108 to approaching uphill and downhill grades. By doing so, the in-vehicle computing system 110 may be able to use information about what final elevation changes will be along the road to more accurately generate the depletion distance prediction results. In some embodiments, the vehicle assistance server 102 calculates a depletion distance estimate for the vehicle 108 based on information received from the in-vehicle computing system 110. The vehicle assistance system 100 allows the control system and prediction system of the vehicle 108 to be proactive, not merely reactive, in the face of changing road conditions, such as changes in road grade, changes in road surface, or changes in meteorological conditions.

An illustrative embodiment of the vehicle assistance server 102 is shown in FIG. 2. Vehicle assistance server 102 is configured to collect crowd-sourced road data and generate vehicle assistance data for vehicles 108 traveling along road 106. The vehicle assistance server 102 includes a processor 220, an I/O subsystem 222, a memory 224, and a data storage device 226. The server 102 may be embodied as any type of computing or computer device capable of performing the functions described herein, including but not limited to a computer, multiprocessor system, server, rack server, blade server, laptop, notebook, network appliance, distributed computing system, processor-based system, and/or consumer electronics device. Of course, in other embodiments, the server 102 may include other or additional components, such as components commonly found in server devices (e.g., various input/output devices). Additionally, in some embodiments, one or more of the illustrative components may be incorporated in, or otherwise form a part of, another component. For example, in some embodiments, the memory 224, or portions thereof, may be incorporated into the processor 220.

Processor 220 may be implemented as any type of processor capable of performing the functions described herein. For example, processor 220 may be implemented as a single or multi-core processor(s), digital signal processor, microcontroller, or other processor or processing/control circuit. Similarly, the memory 224 may be embodied as any type of volatile or non-volatile memory or data storage device capable of performing the functions described herein. In operation, the memory 224 may store various data and software used during operation of the server 102, such as operating systems, applications, programs, libraries, and drivers. Memory 224 is communicatively coupled to processor 220 via an I/O subsystem 222, which may be implemented as circuitry and/or components to facilitate input/output operations with processor 220, memory 224, and other components of server 102. For example, I/O subsystem 222 may be embodied as or otherwise include a memory controller hub, an input/output control hub, firmware devices, communication links (i.e., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.), and/or other components and subsystems to facilitate input/output operations. In some embodiments, I/O subsystem 222 may form part of a system on a chip (SoC) and may be combined with processor 220, memory 224, and other components of server 102 on a single integrated circuit chip.

The data storage device 226 may be implemented as any type of device or devices configured for short or long term storage of data, such as, for example, memory devices and circuits, memory cards, hard drives, solid state drives, or other data storage devices. The data storage device 226 may store compressed and/or decompressed data processed by the server 102.

The server 102 may also include a communication subsystem 228, which may be embodied as any communication circuit, device, or collection thereof that enables communication between the server 102 and other remote devices via a computer network, such as the in-vehicle computing system 110. The communication subsystem 228 may be configured for communication using any one or more communication technologies (e.g., wired or wireless communication) and associated protocols (e.g., ethernet, etc.),

WiMAX, etc.) to enable such communication. The server computing device may include other peripheral devices necessary to perform the functions of the server, such as a display, keyboard, other input/output devices, and other peripheral devices.

The vehicle assistance server 102 may be connected to one or more infrastructure sensors 112 or other sensors 114. The infrastructure sensors 112 and other sensors 114 may be implemented as any type of computing or computer device capable of performing the functions described herein. The infrastructure sensors 112 may include any sensors that measure conditions or otherwise generate sensor data related to or indicative of the condition of the roadway. For example, the infrastructure sensors 112 may include traffic cameras, meteorological sensors such as precipitation sensors and temperature sensors, speed sensors, vibration sensors, wind speed sensors, and other types of sensors. Other types of sensors 114 for measuring conditions of the road system 106 may include cameras and radar on an aircraft (e.g., a drone). The

sensors

112, 114 are configured to detect a road condition and may include, for example, cameras, motion sensors, temperature sensors, radar, microphones, position sensors, and other sensing devices.

An illustrative embodiment of an in-vehicle computing system 110 is shown in FIG. 3. The in-vehicle computing system 110 is configured to sense a condition associated with the vehicle 108 and adjust a vehicle parameter based on vehicle assistance data received by the vehicle assistance server 102. The in-vehicle computing system illustratively includes a processor 320, memory 322, an I/O subsystem 324, a data storage device 334, and a communication device 336. In-vehicle computing system 110 may be embodied as any type of computing or computer device capable of performing the functions described herein, including but not limited to a computer, multiprocessor system, server, rack-mounted server, blade server, laptop, notebook, network appliance, distributed computing system, processor-based system, and/or consumer electronics. In general, components of in-vehicle computing system 110 have the same or similar names as components of server 102 described above and may be similarly implemented. Accordingly, discussion of these similar components is not repeated here.

The illustrative embodiment of in-vehicle computing system 110 includes output device 326, data storage device 334, communication device 336, and sensor 338. Output devices 326 may include a user interface 328, a display 330, speakers 332, and/or other output devices (e.g., vehicle control devices). As discussed below, the output device 326 may be used to notify the driver of the content of the vehicle assistance data received from the vehicle assistance server. Additionally, in some embodiments, the in-vehicle computing system 110 may control the one or more output devices 326 to automatically adjust vehicle parameters in response to receiving vehicle assistance data including vehicle control commands. The vehicle parameters may be embodied as any characteristic or condition of the vehicle that may be adjusted by the in-vehicle computing system 110, such as a throttle of the vehicle 108 during cruise control. The vehicle control commands are generated when the vehicle assistance server 102 determines vehicle parameters that may be changed to improve the driving experience and the vehicle assistance server 102 determines that the vehicle parameters may be changed by the in-vehicle computing system 110 without input from the driver. For example, if the vehicle assistance server 102 determines that the vehicle 108 is approaching an uphill grade on a road, a vehicle control command may be sent to the vehicle 108 to increase the throttle of the vehicle 108 before the vehicle 108 reaches a hill to ensure smooth and rapid ride on the road grade.

The user interface 328 may include any combination of input/output devices such as buttons or a computer touch screen. The input devices of the in-vehicle computing system 110 may include a touch pad or buttons, a compatible computing device (e.g., a smartphone or control unit), voice recognition, gesture recognition, eye tracking, or a brain-computer interface. Display 330 may be implemented as any type of display, such as an in-vehicle display. The speaker 332 may provide audible output to the driver of the vehicle 108 and may produce various sounds indicative of particular events. In some embodiments, the in-vehicle computing system 110 may also include any tactile or olfactory output necessary to change a vehicle parameter or notify the driver of a change in condition. The output devices 326 (user interface 328, display 330, or speaker 332) discussed above are discussed only as alternative embodiments, and additional or other output devices may be present.

In-vehicle computing system 110 also includes sensors 338 configured to sense or measure vehicle operating data and/or road condition data. In some embodiments, one or more cameras 340 may be coupled to the in-vehicle computing system 110 to capture one or more aspects of the roadway. For example, some vehicles 108 may include cameras that monitor stripes on the road surface, allowing the in-vehicle computing system to assist the driver in staying within the traffic lane. In another example, the camera 340 may be used as a gaze detector for determining whether the driver of the vehicle 108 is looking while driving. Microphone 342 may also be included in the in-vehicle computing system 110 to capture sounds made by the vehicle 108 or the driver, such as a driver's voice command, or the microphone may pick up road noise and engine noise from which the vehicle computing system 110 may determine the speed of the vehicle 108.

The in-vehicle computing system 110 may also include a location sensor 344 for determining a location of the vehicle 108. The vehicle assistance server 102 uses the position data measured by the position sensor 344 to determine the position of the vehicle 108, and, for example, the road on which the vehicle 108 is traveling. The sensed location data is useful for building a crowd-sourced database of road data. Additionally, the sensors 338 may include radars 346 coupled to the in-vehicle computing system 110 for measuring the speed of the vehicle 108 and the distance of the vehicle 108 from other objects (e.g., other vehicles). The in-vehicle computing system 110 may also include a laser 348 for determining various things about the vehicle 108. For example, laser 348 may be a lidar configured to determine a distance between vehicle 108 and other vehicles using a roadway.

Of course, the in-vehicle computing system 110 may include other sensors for determining various things about the vehicle 108 and/or the road. For example, in some embodiments, the sensor 338 may include an accelerometer for measuring movement of the vehicle 108, a monitor for measuring brain activity of the driver, or a monitor for measuring temperature of the driver. Other sensors may include a tire pressure sensor, a blind spot monitor, an air-fuel ratio gauge, a crankshaft position sensor, a curb detector for warning the driver of a curb, an engine coolant temperature sensor for measuring engine temperature, a Hall effect sensor, a manifold absolute pressure sensor for regulating fuel metering, a mass flow sensor, an oxygen sensor for measuring the amount of oxygen in the exhaust, a park sensor for warning the driver of an obstacle during a parking maneuver, a radar gun, a speedometer, a throttle position sensor, a torque sensor, a transmission fluid temperature sensor, a turbine speed sensor, a variable reluctance sensor, a water sensor, a wheel speed sensor, a coolant level sensor, an air cleaner temperature sensor, an air pressure sensor, a knock sensor for determining whether the engine is knocking, an ambient air temperature sensor, a knock sensor, a sensor for detecting the presence of an engine knock, a sensor, A heater core temperature sensor, an oil temperature sensor, a transmission gear sensor, an EGR pressure feedback sensor, an accelerator pedal position sensor, a brake pedal position sensor, a transmission output speed sensor, a turbo boost sensor, an acceleration sensor, a brake fluid level sensor, a wash fluid level sensor, a vehicle height sensor, a rear wheel level sensor, a steering rate sensor, a tail light off sensor, an EGR valve position sensor, an occupant determination sensor (e.g., a weight sensor in one or more seats of a vehicle), a cargo weight determination sensor, a battery life determination sensor, a power generation sensor for determining an electrical output of an alternator of a vehicle, a gyroscope, or an ambient light sensor. Thus, it should be understood that the particular sensors 338 (camera 340, microphone 342, position sensor 344, radar 346, laser 348, or other sensors 350) discussed above are merely possible or alternative embodiments, and that additional or other sensors may be incorporated into the in-vehicle computing system 110.

Referring to FIG. 4, in the illustrative embodiment, the vehicle assistance server 102 establishes an environment 400 during operation. The illustrative embodiment 400 includes a data collection module 402, a vehicle assistance module 414, and a communication module 434. In use, the vehicle assistance server 102 is configured to collect and aggregate crowd-sourced road data, collect specific trips with the vehicle 108, and determine vehicle assistance data based on the crowd-sourced road data and vehicle profile information to assist the vehicle in operation of the vehicle 108. The various modules of environment 400 may be implemented as hardware, firmware, software, or a combination thereof. For example, the various modules, logic, and other components of environment 400 may form a portion of or otherwise be established by processor 220 or other hardware components of server 102. As such, in some embodiments, any one or more of the modules of environment 400 may be implemented as a circuit or collection of electrical devices (e.g., vehicle accessory circuits, communication circuits, etc.).

Data collection module 402 is configured to collect and organize crowd-sourced road data from various sources. Crowd sourcing data involves sensing large amounts of data to collect large enough data sets for determining relevant information. For example, by tracking the geographic location (including elevation) of hundreds of vehicles traveling along a particular road, a computing system will be able to more accurately determine the road grade at any particular point on the road. While road grade is not measured directly, it may be determined from other information collected through crowd sourcing. The data collection module 402 includes an infrastructure data module 404, a vehicle data module 406, and a data aggregation module 408.

The infrastructure data module 404 is configured to receive data from one or more infrastructure sensors 112 and other sensors 114. The infrastructure sensors 112 sense data at locations on or near the roadway. For example, the infrastructure sensors 112 may collect information that may be used to determine one or more road conditions, such as road grade, road surface type, or other road hazards (e.g., traffic congestion or vehicle collisions). The infrastructure data module 404 receives the infrastructure data and stores the infrastructure data in the vehicle assistance database 412.

The vehicle data module 406 is configured to receive vehicle data from one or more vehicles 108. The vehicle data may include vehicle identification data, vehicle operation data, and/or driver profile data. The vehicle identification data may include permanent information about the vehicle, such as the manufacturer, model, year, engine specifications, sensors available on the vehicle 108, control systems operating on the vehicle, and other information. The vehicle identification data may also include information about the past performance of the vehicle 108, including, for example, historical data about miles per gallon, historical data of commonly traveled roads, and other long-term trends that may be measured by the vehicle computing system 110. In some embodiments, the vehicle assistance server 102 may generate a probabilistic model based on past performance of the vehicle 108 to determine a probability of future performance or behavior of the vehicle 108. For example, the probabilistic model may indicate a probability that the vehicle 108 will take one or more particular routes based on past routes taken by the vehicle.

The vehicle operating data of the vehicle data may include data regarding current operating characteristics of the vehicle 108, such as data related to a particular trip that the vehicle 108 is currently traveling. For example, if the driver has entered the destination, the vehicle operation data may include route information. In another example, the vehicle operation data may include the weight of the vehicle 108 (including passengers and their equipment), the current location of the vehicle 108, the current speed of the vehicle 108, the current direction of travel of the vehicle 108, and other information. The driver profile information may include information about the driver, such as whether the driver is an aggressive or conservative driver. Driver profile data is not always available and may not always be included in the vehicle profile information or vehicle data. In some embodiments, the driver profile data may include information related to more than one driver of the vehicle 108. For example, the vehicle 108 may have multiple potential drivers in addition to two primary drivers. In this example, the driver profile data will include information about all potential drivers of the vehicle 108 (e.g., the driving style of each driver), and the driver profile data will identify the driver currently operating the vehicle 108.

The data aggregation module 408 collects all received data, including infrastructure data from the infrastructure data module 404 and vehicle data from the vehicle data module 406, and aggregates or organizes the collected data into a searchable database (i.e., crowd-sourced road data). Once the collected road data has been organized, the aggregated road data is stored in the vehicle assistance database 412. In some embodiments, the data aggregation module 408 includes a geographic location module 410 to determine a location of each piece of data collected and associate the data collected with a location on a navigation map. For example, crowd-sourced road data on vehicle assistance database 412 may be indexed by the location where the road data is collected. Indexing the collected road data by location allows the data collection module 402 to create a navigation map that is full of additional information, such as road grade, road surface and adhesion, local weather information, or even specific road hazards. In some embodiments, the data aggregation module 408 divides the road into a plurality of road segments based on the received crowd-sourced data. For example, a road segment may be defined as a road segment having an uphill gradient, while an adjacent road segment may be defined as a road segment having a downhill gradient. Additionally, in some embodiments, the data aggregation module may generate, update, or otherwise maintain one or more probabilistic models based on crowd-sourced road data that may be used to predict the operation or behavior of a vehicle currently traveling on a respective road.

In some embodiments, the crowd-sourced road data includes information about when the crowd-sourced road data is sensed or received. More recently sensed road data may generally be more relevant to the current road conditions than more distant road data. As crowd-sourced road data becomes stale, the information reflected in the crowd-sourced road data may not be as accurate as before. For example, if crowd-sourced road data indicates that the road surface is slippery from snow, the information may not be relevant after six months. Thus, in some embodiments, the data aggregation module 408 may be configured to apply a weighting factor to the collected road data used to generate crowd-sourced road data based on the date the road data was collected. For example, data collected from vehicle sensors and infrastructure sensors one week ago may be more heavily weighted than data collected one month ago. Further, in some embodiments, road data that has become stale beyond a threshold time may be discarded altogether.

The vehicle assistance database 412 is configured to store any information associated with the vehicle assistance system 100. In particular, vehicle assistance database 412 may store crowd-sourced road data collected by data collection module 402. The vehicle assistance database 412 is connected to the vehicle assistance module 414 and is configured for providing information to the vehicle assistance module 414 in order to determine vehicle assistance data. The vehicle assistance database 412 may be implemented as part of the vehicle assistance server 102 or may be external to the server 102 and connected to the server 102 via one or more computer networks. In some embodiments, the vehicle assistance database 412 is the data storage device 226.

The vehicle assistance module 414 is configured to determine vehicle assistance data for vehicles 108 traveling along the road based on current vehicle profile information and crowd-sourced road data stored in the vehicle assistance database 412. The vehicle assistance module 414 includes a vehicle information module 416, a vehicle mission determination module 418, a vehicle assistance data determination module 422, and a notification module 432.

The vehicle information module 416 is configured to receive current vehicle profile information. In some embodiments, the vehicle information module 416 receives vehicle profile information from the vehicle 108 when the vehicle 108 is located on the first road segment. As described above, the vehicle profile information may include vehicle identification data, vehicle operating data, and/or driver profile data. The vehicle information module 416 is configured to receive and organize vehicle profile information into a useful format for the vehicle assistance module 414. For example, the vehicle information module 416 may determine capabilities of the vehicle 108, such as whether the vehicle 108 is capable of cruise control, whether the vehicle 108 is capable of unmanned driving, or whether the in-vehicle computing system 110 is capable of any automatic control features. Another example of information that the vehicle information module 416 may determine is the current location of the vehicle, the travel speed of the vehicle 108, the direction of travel of the vehicle 108, and the road along which the vehicle 108 is traveling. Other types of information that may be determined are the person driving the vehicle 108, the number of passengers in the vehicle 108, the weight of passengers and cargo in the vehicle 108, and other factors that may affect the performance of the vehicle 108.

The vehicle task determination module 418 is configured to receive current vehicle information from the vehicle information module 416 and determine which tasks of the vehicle 108 the vehicle assistance server 102 is capable of assisting. For example, not all vehicles are equipped with all functions. Luxury vehicles typically have several advanced control functions that are not present on low end or old vehicle models. The vehicle mission determination module 418 determines the capabilities of each vehicle using the vehicle assistance system 100. For example, the vehicle mission determination module 418 may determine that a luxury vehicle is capable of using certain advanced driver assistance systems while an older vehicle is only capable of using standard cruise control systems. When the vehicle task determination module 418 determines which tasks the vehicle 108 is capable of performing, the vehicle task determination module generates a task list and the vehicle assistance data determination module 422 generates only vehicle assistance data relevant to the task list. In some embodiments, the vehicle task determination module 418 includes a road segment module 420 for dividing the road on which the vehicle 108 is traveling into a plurality of road segments. The road segment module 420 uses current vehicle information to break up the road into segments according to the tasks to be performed by the vehicle assistance system 100.

The vehicle assistance data determination module 422 is configured to determine vehicle assistance data for any number of vehicles 108 using any number of roads based on crowd-sourced road data. For example, the vehicle assistance data may include vehicle control commands and driver notifications. The vehicle control commands may be implemented as signals that cause the in-vehicle computing system 110 to automatically adjust vehicle parameters. For example, the vehicle control commands may be implemented as commands for the in-vehicle computing system 110 to increase the throttle of the vehicle cruise control system in anticipation of an upcoming uphill grade. The driver notification may be embodied as any type of notification transmitted to the driver by the vehicle assistance server 102. For example, if the vehicle 108 is not in cruise control mode, the driver notification may inform the driver that an uphill grade of the road is approaching and advise the driver to increase the throttle of the engine to increase the power of the engine. Another example of driver notification may include dynamically updating vehicle assistance data of the depletion distance prediction based on upcoming road grade changes in road grade, and outputting the updated depletion distance prediction to the driver.

The vehicle assistance data may be used by an in-vehicle computing device 110 of the vehicle 108 to facilitate the vehicle 108 traversing a roadway. The vehicle assistance data determination module 422 may include a road condition determination module 424, a refueling prediction module 426, a cruise control module 428, and a driver assistance module 430. Of course, in other embodiments, the vehicle assistance data determination module 422 may include additional or other modules.

The road condition determination module 424 is configured to determine one or more conditions of the road using crowd-sourced road data from the vehicle assistance database 412. The condition of the road may include road grade, road surface type, or road hazard. The road condition determination module 424 may determine the slope of the road by correlating the map data with location data obtained from one or more vehicles 108 traveling on the road. Once the road grade of the road is determined, the road condition determination module 424 generates road grade data for the road. The road condition determination module 424 may also determine road surface data indicative of characteristics of the surface of the road. The characteristics of the pavement may include the type of road (e.g., whether the road is paved with concrete or asphalt, or whether the road is soiled) and whether the pavement is affected by environmental factors such as rain, snow, or ice. The road condition determination module 424 may use meteorological data collected from one or more infrastructure sensors 112 or one or more in-vehicle computing systems 110 to determine whether environmental conditions affect the quality of the roadway for driving. The road condition determination module 424 may also determine road hazard data indicative of one or more hazardous conditions of the road. The one or more dangerous conditions of the roadway may include potholes, traffic jams, vehicle crashes, slippery conditions, animals on the roadway (e.g., deer), or other hazards.

Once the road condition data is determined, the vehicle assistance data determination module 422 generates vehicle assistance data for each vehicle 108 using the vehicle assistance system 100. Vehicle assistance is customized for each individual vehicle by using vehicle profile information to determine what response the vehicle 108 should take in response to road hazards. For example, if a heavily loaded vehicle 108 approaches a steep uphill grade on a road, the vehicle assistance data may instruct the vehicle 108 to enter a right-hand lane, downshift to a lower gear, and turn on a hazard flasher of the vehicle 108 to alert other drivers that the vehicle 108 is decelerating. In another example, if the road condition data indicates that a particular road is very slippery, and the vehicle 108 does not have four-wheel drive, the vehicle assistance data may indicate that the vehicle 108 should consider taking an alternate route. In some embodiments, the vehicle assistance data may be used by an in-vehicle computing device 110 of the vehicle 108 to facilitate the vehicle 108 traversing the road segment.

In some embodiments, the vehicle assistance data determination module 422 takes into account information of nearby vehicles participating in the vehicle assistance system 100 when generating vehicle assistance data for each vehicle 108. For example, the vehicle assistance data determination module 422 may consider traffic information when determining the individual vehicle determination data. For example, if a fast vehicle is approaching a slow vehicle, the vehicle assistance data may indicate that the slow vehicle is moving through driver notification to be polite to the high speed vehicle. In another example, the vehicle assistance data determination module 422 may receive information from one vehicle 108 that a pothole is present on the road. Using this information, the vehicle assistance data generated for another vehicle 108 may include notifications regarding upcoming potholes. In this manner, the vehicle assistance system 100 facilitates the advanced sensing capabilities of the luxury vehicle to be shared with other vehicles that may not have such advanced sensing capabilities.

The refueling prediction module 426 is configured to improve the depletion distance prediction output to the driver of the vehicle 108. The refueling prediction module 426 uses the vehicle profile information (including vehicle specifications and vehicle driving history) and road condition data determined using crowd-sourced road data to more accurately predict the amount of travel distance that the vehicle 108 can travel before refueling is needed. For example, if the electric vehicle 108 is traveling along a hilly road, the depletion distance prediction may vary greatly depending on whether the vehicle 108 is traveling uphill or downhill. To present a more accurate prediction, the vehicle assistance data may include a new depletion distance prediction incorporating road grade data for a distance of the predicted route that may be used by the vehicle 108. For example, the new depletion distance prediction may include an estimate based on the next ten mile road, taking into account the approaching uphill and downhill grades on the road. In some embodiments, the refueling prediction module 426 uses the predicted route selection to determine the road on which the vehicle 108 is likely to travel next. For example, a typical vehicle 108 typically travels only to a number of limited locations, such as between work and home. Using the past route data of the vehicle 108 and the past fuel performance along the route (i.e., how much fuel the vehicle 108 uses when traversing a particular road segment), the refueling prediction module 426 may make a more accurate depletion distance prediction. In some embodiments, the refueling prediction module 426 uses fuel usage data collected from other vehicles 108 participating in the vehicle assistance system 100 to make more accurate depletion distance predictions. For example, the refueling prediction module 426 may use information from similar types of vehicles 108 when making a new depletion distance prediction (e.g., use fuel usage from other commodity vehicles when determining what the depletion distance prediction of nature of the birth (Altima) should be). In some embodiments, the refueling prediction module 426 may use the driver profile data to determine whether the driving style of the driver of the vehicle 108 will affect the distance the vehicle 108 can travel before refueling is needed. For example, a driver driving in an aggressive style may need to be refueled more frequently due to a reduction in miles per gallon caused by high speed and frequent acceleration of the vehicle 108.

The cruise control module 428 is configured to generate vehicle control commands related to cruise control of the vehicle 108. In some embodiments, the cruise control data may be used by an on-board computing system 110 of the vehicle 108 to adjust a throttle of an engine of the vehicle 108. For example, if the road condition data indicates that the vehicle 108 is approaching an uphill grade, the cruise control module 428 may generate vehicle assistance data that instructs the cruise control system of the vehicle 108 to apply more throttle to the engine before the vehicle 108 encounters an uphill grade. Cruise control module 428 generates vehicle control commands in anticipation of upcoming changes in road conditions and allows the occupants of vehicle 108 to experience a smooth ride. In some embodiments, the cruise control module 428 generates cruise control data for road segments that the vehicle 108 is traveling toward but has not yet traversed.

The driver assistance module 430 is configured for generating one or more vehicle control commands related to an advanced driver assistance system. For example, even if the automobile is not in cruise control mode, the vehicle assistance data may include vehicle control commands for an advanced driver assistance system of the vehicle 108 to increase the power of the engine when the vehicle 108 begins to climb an uphill grade of the road. In another example, the driver assistance module 430 may be configured to augment the suspension of the vehicle 108 in response to road condition data indicating that a pothole is approaching.

The notification module 432 is configured for generating a driver notification output to be communicated to a driver of the vehicle 108 in response to generating the vehicle assistance data. When the vehicle assistance data is transmitted to the in-vehicle computing system 110, a driver notification may be included in the vehicle assistance data. In some embodiments, a notification is generated for the driver each time vehicle assistance data is transmitted to the in-vehicle computing system 110. In some embodiments, no notification is output to the driver of the vehicle 108 when the vehicle assistance data includes only vehicle control commands. The notification may include a recommendation of an action that the driver should take in control of the vehicle 108.

The communication module 434 is configured to allow the vehicle assistance server 102 to communicate with one or more in-vehicle computing systems 110, one or more infrastructure sensors 112, and one or more other sensors 114 over the network 104. The communication module 434 is configured to process all of the different types of data processed by the vehicle assistance server 102 and corresponding to the communication subsystem 228. The communication module 434 may be configured to communicate using any one or more communication technologies (e.g., wired or wireless communication) and associated protocols (e.g., ethernet, etc.),

WiMAX, etc.) to enable such communication.

Referring to FIG. 5, in an illustrative embodiment, the in-vehicle computing system 110 establishes an environment 500 during operation. The illustrative embodiment 500 includes a vehicle profile module 502, a vehicle output module 506, a sensor management module 516, and a communication module 518. In use, the in-vehicle computing system 110 is configured to collect sensor data from the sensors 338, transmit vehicle profile information to the vehicle assistance server 102, receive vehicle assistance data from the vehicle assistance server 102, and modify vehicle output based on the vehicle assistance data. The various modules of environment 500 may be implemented as hardware, firmware, software, or a combination thereof. For example, the various modules, logic, and other components of environment 500 may form part of or otherwise be established by processor 320 or other hardware components of in-vehicle computing system 110. As such, in some embodiments, any one or more of the modules of environment 500 may be implemented as a circuit or collection of electrical devices (e.g., a vehicle profile circuit, a vehicle output circuit, a sensor management circuit, a communication circuit, etc.).

The vehicle profile module 502 is configured to collect, store, and transmit vehicle profile information. The vehicle profile information may include driver profile data, vehicle identification data, and/or vehicle operating data. As discussed above, the vehicle identification data generally includes information about the vehicle 108 relating to a permanent characteristic. For example, the vehicle identification data may include the manufacturer, model and year of the vehicle 108, as well as other specifications of the automobile as desired. In some embodiments, the vehicle identification data includes long-term historical data regarding the operation of the vehicle 108, such as total miles per gallon, miles traveled by the vehicle 108, or routes typically taken by the vehicle 108. The vehicle operation data may include any information indicative of the operation of the vehicle 108 as the vehicle 108 traverses a road segment, or in other words, as the vehicle 108 participates in a particular trip to a particular destination. The vehicle operating data generally includes data collected by the sensors 338 during operation of the vehicle 108 during a particular trip. For example, the vehicle operation data may include a current location of the vehicle 108, a speed of the vehicle 108, a direction of travel of the vehicle 108, and/or other data related to a particular trip of the vehicle 108. The vehicle profile module 502 may include a driver profile module 504. The driver profile module 504 is configured to collect, store, and transmit driver profile data. For example, the driver profile may include driver status and information about whether the driver is a conservative or aggressive driver. The vehicle assistance server 102 is capable of customizing the vehicle assistance data of the vehicle 108 according to the driver profile data received from the in-vehicle computing system 110.

The vehicle output module 506 is configured to implement the commands indicated by the vehicle assistance data. For example, the vehicle output module 506 may output information to one or more vehicle output devices 326 of the in-vehicle computing system 110. Such output devices 326 may include a display, speakers, or other vehicle systems that may receive vehicle control commands. Additionally or alternatively, the vehicle output module 506 may output control signals to one or more of the vehicle output devices 326 to control operation thereof.

The illustrative vehicle output modules 506 include a refueling prediction module 508, a cruise control module 510, a driver assistance module 512, and a notification module 514. Of course, in other embodiments, the vehicle output module 506 may include additional or other sub-modules. The refueling prediction module 508 is configured to calculate a depletion distance prediction result based on vehicle assistance data received from the vehicle assistance server 102. For example, the vehicle assistance data may include information about road conditions, including the grade of the upcoming road segment. Using the road slope data, the refueling prediction module 508 may calculate a depletion distance prediction result that includes upcoming road slope data, thereby making a more accurate depletion distance prediction. In some embodiments, the vehicle assistance server 102 calculates the depletion distance prediction and the refueling prediction module 508 outputs only the depletion distance prediction to the driver of the vehicle 108.

The cruise control module 510 is configured to receive vehicle assistance data including vehicle control commands related to cruise control of a vehicle. For example, the vehicle control commands may include a command that the in-vehicle computing system 110 apply more power to the engine before the vehicle 108 reaches an upcoming uphill grade. In some embodiments, the vehicle output module 506 will only execute vehicle control commands related to cruise control if the vehicle 108 is actively using cruise control.

The driver assistance module 512 is configured to output any other vehicle control commands that may be included in the vehicle assistance data received from the vehicle assistance server 102. For example, the driver assistance module 512 may be configured to output vehicle controls related to advanced driver assistance systems of the vehicle 108.

The notification module 514 is configured to output a driver notification to a driver of the vehicle 108 in response to receiving the vehicle assistance data. In some embodiments, a notification is sent to the driver whenever the in-vehicle computing system 110 receives vehicle assistance data. In some embodiments, when the vehicle assistance data includes only vehicle control commands, no notification is output to the driver of the vehicle. The notification may include a recommendation of an action that the driver should take in control of the vehicle 108. The notification may be output to the driver using one of the output devices 326. For example, the written message may be output to the display 330 of the vehicle 108 indicating what information the vehicle assistance data contains. In some embodiments, the notification includes an audible signal, such as a beep or jingle, to alert the driver that the message is being output. In some embodiments, the notification is output to a speaker 332 of the vehicle 108 so that the driver can hear the notification.

The sensor management module 516 is configured to manage sensors integrated with the in-vehicle computing system 110. The sensor management module 516 records all data collected by the sensors 338 (which includes vehicle operation data) and transmits the vehicle operation data to the vehicle assistance server 102 via the communication module 518.

The communication module 518 is configured to allow the in-vehicle computing system 110 to communicate with the vehicle assistance server 102 over the network 104. The communication module 518 is configured to process all of the different types of data processed by the in-vehicle computing system 110 and corresponding to the communication device 336. The communication module 518 may be configured to use any one or more communication technologies (e.g., wired or wireless)Wire communication) and associated protocols (e.g., ethernet, etc.),

WiMAX, etc.) to enable such communication.

Referring to fig. 6, in use, the vehicle assistance server 102 may perform a method 600 for generating crowd-sourced road data. At block 602, the vehicle assistance server 102 receives, through various sources, road data indicative of at least one characteristic of a road or road segment traversed by each of the plurality of vehicles. For example, at block 604, the vehicle assistance server 102 may receive infrastructure data sensed or generated by the infrastructure sensors 112 and/or other sensors 114. The infrastructure data may be embodied as any type of data indicative of the condition or characteristics of a road (e.g., road segment). The particular types of infrastructure data sensed by the

sensors

112, 114 may include, for example, location data 606, weather data 608, and/or road closure data 610. The location data 606 may be embodied in relation to the particular location at which the

sensor

112, 114 senses the data. Weather data 608 may be embodied as any information indicative of weather, weather patterns, and/or weather forecasts. For example, meteorological data may include temperature data, precipitation data, wind speed data, barometric pressure data, or other data. Road closure data 610 may be embodied as any information related to road closure, such as vehicle collisions, closures for meteorological reasons, delays due to traffic, landslides, and the like. Of course, it should be understood that in other embodiments, the vehicle assistance server 102 may receive other types of infrastructure data indicative of road conditions or characteristics.

At block 612, the vehicle assistance server 102 may receive vehicle data sensed or generated by one or more vehicle sensors 338 associated with the in-vehicle computing systems 110 of one or more vehicles 108 traversing the road system 106. The vehicle data may be embodied as any type of data indicative of a characteristic, condition, or quality of the vehicle 108 and/or a user of the vehicle 108. The particular types of vehicle data may include, for example, driver profile data 614, vehicle identification data 616, and/or vehicle operation data 618. As discussed above, the driver profile data 614, the vehicle identification data 616, and the vehicle operation data 618 generally relate to information about the vehicle 108. In particular, the driver profile data 614 may be embodied as any information known about the driver of the vehicle 108 (e.g., driver identity, driving restrictions, whether the driver's driving style is aggressive or conservative, etc.); the vehicle identification data 616 may be embodied as information related to permanent characteristics of the vehicle 108 (e.g., the year, manufacturer, and model of the vehicle 108); and the vehicle operation data 618 may be embodied as information related to vehicle parameters for a particular trip of the vehicle 108 (e.g., a current location of the vehicle 108, a current weight of the vehicle 108, etc.).

At block 620, the vehicle assistance server 102 aggregates the received roads into crowd sourced road data. In some embodiments, crowd sourced road data is stored in a searchable database, such as vehicle assistance database 412. In some embodiments, the road data is indexed by the location at which the sensor senses the data. For example, at block 622, the vehicle assistance server 102 determines geographic location data for each piece of road data collected. The geo-location data relates to the exact location at which the respective sensors (e.g., infrastructure sensors 112, other sensors 114, sensors 338 of in-vehicle computing system 110, etc.) sensed the data. The vehicle assistance server 102 also determines road location data including the geographic location of the road. Of course, it should be understood that not all data may be sensed directly on the roadway, e.g., the infrastructure sensors 112 may be provided separate from the roadway. Thus, the vehicle assistance server 102 may associate these data components of crowd-sourced road data with nearby road locations. As such, at block 624, the vehicle assistance server 102 links crowd-sourced road data to road locations using the geographic location data and the road location data. In some embodiments, the road data is also indexed using the time at which the road data was sensed. For example, meteorological data indicating snow on a road will lose relevance and accuracy over time.

Referring now to fig. 7, in use, the vehicle assistance server 102 may perform a method 700 for determining vehicle assistance data for a particular vehicle. At block 702, the vehicle assistance server 102 receives vehicle data from the vehicle 108. In addition to providing information identifying characteristics of the vehicle 108 and/or the vehicle operator, the vehicle profile information also provides an indication to the vehicle assistance server 102 that the in-vehicle computing system 110 of the vehicle 108 is ready to receive vehicle assistance data. In some embodiments, the vehicle 108 may provide vehicle data to be used in determining road data without any vehicle assistance data in exchange. In such embodiments, the vehicle 108 is a passive data collector and vehicle assistance data is not used to improve vehicle function. As shown in fig. 7, the vehicle data may include driver profile data 704, vehicle identification data 706, and/or vehicle operation data 708. As discussed above, the driver profile data 704 may be embodied as any type of data. As discussed above, the driver profile data 614, the vehicle identification data 616, and the vehicle operation data 618 generally relate to information about the vehicle 108. In particular, the driver profile data 614 may be embodied as any information known about the driver of the vehicle 108 (e.g., driver identity, driving restrictions, whether the driver's driving style is aggressive or conservative, etc.); the vehicle identification data 616 may be embodied as information related to permanent characteristics of the vehicle 108 (e.g., the year, manufacturer, and model of the vehicle 108); and the vehicle operation data 618 may be embodied as information related to vehicle parameters for a particular trip of the vehicle 108 (e.g., a current location of the vehicle 108, a current weight of the vehicle 108, etc.).

At block 710, the vehicle assistance server 102 determines road condition data based on the vehicle profile information and based on crowd-sourced road data stored in the vehicle assistance database 412. The traffic data is any information indicating one or more traffic conditions of a road or a section of a road. For example, the road condition data may include road grade data 712, road surface data 714, or road hazard data 716. Road grade data 712 may be embodied as any type of data indicative of the slope of a road over a given distance; or in other words the elevation of the road varies over a given distance of the road. The road grade data indicates the grade of the road at any given point. Road slope data 712 may be determined, for example, by determining an accurate road slope by comparing a navigation map, a terrain map, and received crowd sourced data. For example, the location sensors of the in-vehicle computing system 110 track the location of the respective vehicle 108 as it travels along a particular road. After enough vehicles have sent the location data to the vehicle assistance server 102, the vehicle assistance server 102 may analyze the data to find which route is likely the exact path of the road. Using the path data, the vehicle assistance server 102 may be able to determine elevation changes of the road along a section of the road, thereby determining road grade data 712.

Road surface data 714 may be embodied as any type of data indicative of the current road surface type and/or other factors that may affect the quality of the road surface (e.g., snow or ice). The road surface data 714 may be determined by using the navigation map information and crowd-sourced road data to determine what type of road surface is present, such as a concrete road, asphalt road, or dirt road. Further, the road surface data 714 may include information about factors that may affect the road surface quality. For example, the road surface data 714 may include meteorological data to determine whether the road surface is slippery, whether due to ice or snow. In some embodiments, road surface data may be determined using road type data collected from a digital navigation map. For example, many digital navigation maps include indicators regarding the size and paving of a particular road or road segment, such as whether a particular road is a dirt road. Typically, road information may be inferred from road type, for example interstate highways are laid out with concrete. The road surface data may be generated using data from a digital navigation map and crowd sourced road data.

The road hazard data 716 may be embodied as any type of data that indicates whether there is any hazard on the road, and is determined by analyzing crowd sourced road data. Hazards may include potholes, vehicle collisions, traffic jams, or animals on the road (e.g., deer). For example, using the sensors 338 associated with the in-vehicle computing system 110, the vehicle assistance server 102 may be configured to determine whether there is a pothole in the road based on the vehicle 108 indicating that an object was hit, or the vehicle 108 has snatched out of an obstacle. In another example, vehicle sensors indicating objects other than another vehicle and other avoidance maneuvers performed by the driver of the vehicle 108 and indicated by the position sensors of the vehicle 108 may indicate that the deer is in or near the road.

At block 718, the vehicle assistance server 102 generates vehicle assistance data for the particular vehicle 108 based on the road condition data and the vehicle profile information. In some embodiments, the vehicle assistance server 102 first determines which tasks to include in the vehicle assistance data before generating the vehicle assistance data. Determining the task prior to generating the vehicle assistance data allows the vehicle assistance server 102 to communicate the vehicle assistance data to the particular vehicle 108 while conserving computing resources. In some embodiments, the vehicle assistance data may include cruise control data 720, refueling prediction data 722, driver assistance data 724, and/or notification data 726 as discussed above. Cruise control data 720 may include adjustments to the engine throttle based on road condition data. For example, if the road condition data indicates that an uphill road grade is approaching, the cruise control data 720 may include a notification or vehicle control command that the vehicle 108 should increase engine power in preparation for the upcoming road grade. In some embodiments, the cruise control data 720 is determined by the vehicle assistance server 102 only when the vehicle profile information indicates that the vehicle 108 is currently using cruise control. The refueling prediction data 722 may include a depletion distance prediction for the vehicle 108 based on road condition data. For example, if the vehicle 108 is traveling along a section of a road having many hills, the refueling prediction data 722 will use the road condition data and the past performance history of the vehicle 108 (included in the vehicle profile information) to determine an exhausted distance prediction that takes into account the upcoming uphill and downhill slopes. In some embodiments, the refueling prediction data 722 is determined by the vehicle assistance server 102 only when the vehicle information indicates that the vehicle 108 is actively outputting a depletion distance prediction to the driver. Driver assistance data 724 may include adjustments to any number of vehicle parameters controlled by advanced driver assistance systems. For example, an advanced driver vehicle assistance system may comprise a lane change assistant for assisting the driver when changing lanes. In some embodiments, the driver assistance data 724 may include vehicle control commands for indicating to the driver that it is safe to change lanes based on road condition data and vehicle profile information of other vehicles 108. For example, visibility may be impeded when traveling along a two-lane road, so the driver may not know when it may be safe to travel on an upcoming traffic lane and past slower moving vehicles. Driver assistance data 724 may include notifications that inform the driver when safe to pass based on the location of other vehicles on the roadway, even when visibility is limited. Additionally, the vehicle assistance server 102 may send a notification to an approaching vehicle 108 that another vehicle 108 is passing in its traffic lane. The notification data 726 may indicate to the driver the content of the received vehicle assistance data. Notification data 726 may simply include a statement of vehicle parameters that are automatically adjusted by in-vehicle computing system 110, or notification data 726 may include driving recommendations for the driver, e.g., applying more throttle to the engine in preparation for approaching an uphill road grade or other information.

At block 728, the vehicle assistance server 102 transmits the vehicle assistance data to the in-vehicle computing system 110 of the vehicle 108. Upon receiving the vehicle assistance data, the in-vehicle computing system 110 is configured to implement vehicle control commands or notifications included in the vehicle assistance data. At block 730, the vehicle assistance server 102 determines whether the vehicle assistance data just generated for the vehicle is available to other vehicles 108 on the road. If the vehicle assistance data is determined to be not useful to other vehicles 108, method 700 returns to the beginning and begins collecting data again. If the vehicle assistance data is determined to be useful, the vehicle assistance server 102 sends a notification of the relevant road conditions to another vehicle at block 732. For example, these types of notifications may include a warning that a faster vehicle is approaching from behind, or a warning that another vehicle is passing your lane of traffic and please notice. At block 734, vehicle assistance server 102 determines whether to exit method 700.

Referring to fig. 8, in use, the in-vehicle computing system 110 may perform a method 800 for assisting a driver of the vehicle 108 based on vehicle assistance data received from the vehicle assistance server 102. At block 802, the in-vehicle computing system 110 transmits vehicle profile information to the vehicle assistance server 102. In some embodiments, the in-vehicle computing system 110 senses data using the sensor 338 prior to transmitting the vehicle profile information. In some embodiments, the vehicle profile information may include driver profile data 804, vehicle identification data 806, or vehicle operation data 808, as discussed above.

At block 810, the in-vehicle computing system 110 receives vehicle profile information from the vehicle assistance server 102. As discussed above, the vehicle assistance server 102 generates vehicle assistance data based on the crowd-sourced road data and vehicle profile information received from the in-vehicle computing system 110. At block 812, the in-vehicle computing system 110 determines whether the vehicle assistance data includes a vehicle control command. If the in-vehicle computing system 110 determines that a vehicle control command exists, the in-vehicle computing system 110 adjusts the vehicle parameter indicated by the vehicle control command at block 814. The vehicle parameters that may be adjusted by the in-vehicle computing system 110 may include a throttle of the engine or one or more indicator lights. If the in-vehicle computing system 110 determines that a vehicle control command does not exist, at block 816, the in-vehicle computing system generates a notification based on the vehicle assistance data for output to the driver of the vehicle 108. The notification may comprise road data contained in the vehicle assistance data, or a recommendation to the driver about vehicle parameters that the driver should adjust. In some embodiments, when a vehicle control command is present in the vehicle assistance data, the in-vehicle computing system 110 generates a notification based on the vehicle assistance data.

Examples of the invention

Illustrative examples of the techniques disclosed herein are provided below. Embodiments of the technology may include any one or more of the examples described below, or any combination thereof.

Example 1 includes a vehicle assistance server for assisting a driver of a vehicle, the vehicle assistance server comprising: a data collection module to receive road data from each of a plurality of vehicles, wherein the road data includes data indicative of at least one characteristic of a first road segment traversed by each of the plurality of vehicles; a data aggregation module to aggregate the road data received from each of the plurality of vehicles to generate crowd-sourced road data associated with the first road segment; a vehicle information module to receive vehicle profile information from a first vehicle located on the first road segment, wherein the vehicle profile information defines at least one characteristic of the first vehicle; and a vehicle assistance data determination module to determine vehicle assistance data for the first vehicle based on the vehicle profile information and the crowd-sourced road data associated with the first road segment, wherein the vehicle assistance data is usable by an in-vehicle computing device of the first vehicle to facilitate traversal of the first road segment by the vehicle.

Example 2 includes the subject matter of example 1, and wherein the vehicle assistance data determination module is to determine road grade data for the first road segment based on the crowd-sourced road data, wherein the road grade data is indicative of a change in vertical elevation along the first road segment.

Example 3 includes the subject matter of any of examples 1 and 2, and wherein the vehicle assistance data determination module is to determine refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data estimates a distance that the first vehicle can travel before refueling is needed.

Example 4 includes the subject matter of any of examples 1-3, and wherein the refueling prediction data includes an estimated distance of travel until the first vehicle requires refueling based on a grade of the first road segment.

Example 5 includes the subject matter of any of examples 1-4, and wherein the refueling prediction data is usable by an on-board computing device of the first vehicle to adjust the estimated distance until refueling according to a past fuel performance of the first vehicle on the first segment.

Example 6 includes the subject matter of any of examples 1-5, and wherein the vehicle assistance data determination module is to determine cruise control data based on the road grade data and the vehicle profile information, wherein the cruise control data is usable by the in-vehicle computing device to adjust a throttle of an engine of the first vehicle.

Example 7 includes the subject matter of any of examples 1-6, and wherein the cruise control data is usable by the on-board computing device to adjust a throttle of the engine before the first vehicle begins traversing the first segment.

Example 8 includes the subject matter of any of examples 1-7, and wherein the cruise control data is based on the road grade data and information included in the vehicle profile information indicating an operating characteristic of a second vehicle while traversing the first road segment, wherein the second vehicle has at least one permanent characteristic in common with the first vehicle.

Example 9 includes the subject matter of any of examples 1-8, and wherein the vehicle assistance data determination module is to determine refueling prediction data based on the crowd-sourced road data and the vehicle profile information, wherein the vehicle profile information includes driver profile data identifying a driver of the first vehicle from a plurality of potential drivers and includes an indicator of a driving style of the identified driver, and wherein the refueling prediction data estimates a distance the first vehicle can travel before refueling is needed.

Example 10 includes the subject matter of any of examples 1-9, and wherein the data collection module comprises an infrastructure data module to receive infrastructure roadway data from each of a plurality of infrastructure sensors associated with the first road segment, wherein the infrastructure roadway data includes at least one characteristic indicative of the first road segment traversed by each of the plurality of vehicles.

Example 11 includes the subject matter of any of examples 1-10, and wherein the vehicle information module is to receive vehicle identification data indicative of one or more permanent characteristics of the vehicle and vehicle operation data indicative of trip-specific characteristics of the vehicle.

Example 12 includes the subject matter of any of examples 1-11, and wherein the vehicle assistance data determination module is to determine road surface data indicative of a characteristic of a surface of the first road segment.

Example 13 includes the subject matter of any of examples 1-12, and wherein the vehicle assistance data determination module is to determine road type data indicative of a road type of the first road segment from a digital navigation map.

Example 14 includes the subject matter of any of examples 1-13, and wherein the vehicle assistance data determination module is to receive weather data from one or more infrastructure sensors associated with the first road segment, wherein determining the road surface data comprises determining, by the vehicle assistance server, road surface data based on the road type data and the weather data.

Example 15 includes the subject matter of any of examples 1-14, and wherein the vehicle assistance data determination module is to determine road hazard data indicative of one or more hazardous conditions for the first road segment based on the crowd-sourced road data.

Example 16 includes the subject matter of any of examples 1-15, and wherein the vehicle assistance data determination module is to receive operational data from each of the plurality of vehicles traversing the first road segment, and determine a location of one or more potholes located in the first road segment based on the crowd-sourced road data and the vehicle operational data.

Example 17 includes the subject matter of any of examples 1-16, and wherein the vehicle assistance data determination module is to determine one or more vehicle control commands usable by the in-vehicle computing device to adjust one or more vehicle parameters of the first vehicle.

Example 18 includes the subject matter of any of examples 1-17, and wherein the vehicle assistance data determination module is to determine one or more notifications for a driver of the first vehicle to be delivered by an output device of the first vehicle.

Example 19 includes the subject matter of any of examples 1-18, and further comprising a communication module to send one or more notifications to one or more second vehicles traveling on the first road segment, wherein the one or more notifications are to alert the one or more second vehicles of the road condition for the first road segment.

Example 20 includes the subject matter of any of examples 1-19, and wherein the data aggregation module is to generate a probabilistic model based on the crowd-sourced road data; and the vehicle assistance data determination module is to determine the vehicle assistance data based on the vehicle profile information and the probabilistic model.

Example 21 includes the subject matter of any of examples 1-20, and wherein the data collection module is to apply a weighting factor to road data received from each of a plurality of vehicles based on a date the road data was collected by each of the plurality of vehicles.

Example 22 includes an in-vehicle computing system to assist a driver of a first vehicle, the in-vehicle computing system comprising: a vehicle profile module to transmit vehicle profile information of the first vehicle indicative of at least one characteristic of the first vehicle to a vehicle assistance server while the first vehicle is on a first road segment; a vehicle output module to (i) receive vehicle assistance data from the vehicle assistance server, wherein the vehicle assistance data is generated based on the vehicle profile information and crowd-sourced road data associated with the first road segment, (ii) determine the vehicle output module based on the received vehicle assistance data to determine at least one vehicle control command, and (iii) adjust a vehicle parameter of the first vehicle based on the vehicle control command.

Example 23 includes the subject matter of example 22, and further comprising a notification module to generate a notification for a driver of the first vehicle including information related to the vehicle assistance data in response to receiving the vehicle assistance data.

Example 24 includes the subject matter of any of examples 22 and 23, and wherein the vehicle profile computing system is to sense vehicle operation data indicative of operation of the first vehicle while the first vehicle traverses the first road segment, and transmit the vehicle operation data and vehicle identification data indicative of at least one permanent characteristic of the first vehicle.

Example 25 includes the subject matter of any of examples 22-24, and wherein the vehicle output module is to receive cruise control data, and adjusting the vehicle parameter includes adjusting, by the in-vehicle computing system, a throttle of an engine of the first vehicle based on the cruise control data.

Example 26 includes the subject matter of any of examples 22-25, and wherein the vehicle output module is to receive refueling prediction data that estimates a distance the first vehicle can travel before refueling is needed, and adjust a throttle of an engine of the first vehicle based on the cruise control data.

Example 27 includes the subject matter of any of examples 22-26, and wherein the vehicle output module is to receive, from the vehicle assistance server, road status data indicative of one or more road conditions for the first road segment; and generating a notification regarding the one or more road conditions to inform a driver of the first vehicle.

Example 28 includes the subject matter of any of examples 22-27, and wherein the vehicle output module is to receive, from the vehicle assistance server, road grade data indicative of a change in vertical elevation along the first road segment.

Example 29 includes a method for assisting a driver of a vehicle, the method comprising: receiving, by a vehicle assistance server, road data from each of a plurality of vehicles, wherein the road data comprises data indicative of at least one characteristic of a first road segment traversed by each of the plurality of vehicles; aggregating, by the vehicle assistance server, the road data received from each of the plurality of vehicles to generate crowd-sourced road data associated with the first road segment; receiving, by the vehicle assistance server, vehicle profile information from a first vehicle located on the first segment, wherein the vehicle profile information defines at least one characteristic of the first vehicle; determining, by the vehicle assistance server, vehicle assistance data for the first vehicle based on the vehicle profile information and the crowd-sourced road data associated with the first road segment, wherein the vehicle assistance data is usable by an in-vehicle computing device of the first vehicle to facilitate traversal of the first road segment by the vehicle; and transmitting the vehicle assistance data to the first vehicle.

Example 30 includes the subject matter of example 29, and further comprising determining, by the vehicle assistance server, road grade data for the first road segment based on the crowd-sourced road data, the road grade data indicating a change in vertical elevation along the first road segment.

Example 31 includes the subject matter of any of examples 29 and 30, and wherein determining vehicle assistance data includes determining, by the vehicle assistance server, refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data estimates a distance the first vehicle can travel before refueling is required.

Example 32 includes the subject matter of any of examples 29-31, and wherein determining the refueling prediction data includes determining, by the vehicle assistance server, refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data includes an estimated distance traveled until the first vehicle requires refueling based on the determined grade of the first road segment.

Example 33 includes the subject matter of any of examples 29-32, and wherein the refueling prediction data is usable by an on-board computing device of the first vehicle to adjust the estimated distance until refueling according to a past fuel performance of the first vehicle on the first segment.

Example 34 includes the subject matter of any of examples 29-33, and wherein determining vehicle assistance data includes determining, by the vehicle assistance server, cruise control data based on the road grade data and the vehicle profile information, wherein the cruise control data is usable by the in-vehicle computing device to adjust a throttle of an engine of the first vehicle.

Example 35 includes the subject matter of any of examples 29-34, and wherein determining the cruise control data includes determining cruise control data usable by the on-board computing device to adjust a throttle of the engine before the first vehicle begins traversing the first segment.

Example 36 includes the subject matter of any of examples 29-35, and wherein determining the cruise control data includes determining, by the vehicle assistance server, cruise control data based on the road grade data and information included in the vehicle profile information that indicates an operating characteristic of a second vehicle while traversing the first road segment, wherein the second vehicle has at least one permanent characteristic in common with the first vehicle.

Example 37 includes the subject matter of any of examples 29-36, and further comprising determining, by the vehicle assistance server, refueling prediction data based on the crowd-sourced road data and the vehicle profile information, the refueling prediction data estimating a distance the first vehicle can travel before refueling is needed, wherein the vehicle profile information includes driver profile data identifying a driver of the first vehicle from a plurality of potential drivers and includes an indicator of a driving style of the identified driver.

Example 38 includes the subject matter of any of examples 29-37, and further comprising receiving, by the vehicle assistance server, infrastructure road data from each of a plurality of infrastructure sensors associated with the first road segment, wherein the infrastructure road data includes at least one characteristic indicative of the first road segment traversed by each of the plurality of vehicles.

Example 39 includes the subject matter of any of examples 29-38, and wherein receiving vehicle profile information comprises receiving, by the vehicle assistance server, vehicle identification data indicative of one or more permanent characteristics of the vehicle and vehicle operation data indicative of trip-specific characteristics of the vehicle.

Example 40 includes the subject matter of any of examples 29-39, and wherein determining vehicle assistance data includes determining, by the vehicle assistance server, road surface data indicative of a characteristic of a surface of the first road segment.

Example 41 includes the subject matter of any of examples 29-40, and wherein determining the road surface data comprises determining, by the vehicle assistance server, road type data indicative of a road type of the first road segment from a digital navigation map.

Example 42 includes the subject matter of any of examples 29-41, and further comprising receiving, by the vehicle assistance server, meteorological data from one or more infrastructure sensors associated with the first road segment, wherein determining the road surface data comprises determining, by the vehicle assistance server, road surface data based on the road type data and the meteorological data.

Example 43 includes the subject matter of any one of examples 29-42, and further comprising determining, by the vehicle assistance server determination module, road hazard data indicative of one or more hazardous conditions for the first road segment based on the crowd-sourced road data.

Example 44 includes the subject matter of any of examples 29-43, and wherein receiving the road data includes receiving, by the vehicle assistance server, operational data from each of the plurality of vehicles traversing the first road segment, and determining the road hazard data includes determining a location of one or more potholes located in the first road segment based on the crowd-sourced road data and the vehicle operational data.

Example 45 includes the subject matter of any of examples 29-44, and wherein determining vehicle assistance data includes determining, by the vehicle assistance server, one or more vehicle control commands usable by the in-vehicle computing device to adjust one or more vehicle parameters of the first vehicle.

Example 46 includes the subject matter of any of examples 29-45, and wherein determining vehicle assistance data includes determining, by the vehicle assistance server, one or more notifications for a driver of the first vehicle to be delivered by an output device of the first vehicle.

Example 47 includes the subject matter of any of examples 29-46, and further comprising sending, by the vehicle assistance server, one or more notifications to one or more second vehicles traveling on the first road segment, wherein the one or more notifications are to alert the one or more second vehicles of the road condition for the first road segment.

Example 48 includes the subject matter of any of examples 29-47, and further comprising generating a probabilistic model based on the crowd-sourced road data, and wherein determining the vehicle assistance data comprises determining vehicle assistance data based on the vehicle profile information and the probabilistic model.

Example 49 includes the subject matter of any one of examples 29-48, and further comprising applying, by the vehicle assistance server, a weighting factor to road data received from each of a plurality of vehicles based on a date the road data was collected by each of the plurality of vehicles.

Example 50 includes a method for assisting a driver of a vehicle, the method comprising: transmitting, by an in-vehicle computing system of a first vehicle while the first vehicle is on a first road segment, vehicle profile information indicative of at least one characteristic of the first vehicle to a vehicle assistance server; receiving, by the in-vehicle computing system, vehicle assistance data from the vehicle assistance server, wherein the vehicle assistance data is generated based on the vehicle profile information and crowd-sourced road data associated with the first road segment; determining, by the in-vehicle computing system, at least one vehicle control command based on the received vehicle assistance data; and adjusting, by the in-vehicle computing system, a vehicle parameter of the first vehicle based on the vehicle control command.

Example 51 includes the subject matter of example 50, and further comprising generating, by the in-vehicle computing system and in response to receiving the vehicle assistance data, a notification for a driver of the first vehicle comprising information related to the vehicle assistance data.

Example 52 includes the subject matter of any of examples 50 and 51, and wherein transmitting vehicle profile information comprises transmitting, by an in-vehicle computing system, vehicle operation data indicative of operation of the first vehicle while the first vehicle traverses the first road segment; and transmitting, by the in-vehicle computing system, the vehicle operation data and vehicle identification data indicative of at least one permanent characteristic of the first vehicle.

Example 53 includes the subject matter of any of examples 50-52, and wherein receiving vehicle assistance data includes receiving, by the on-board computing system, cruise control data, and adjusting the vehicle parameter includes adjusting, by the on-board computing system, a throttle of an engine of the first vehicle based on the cruise control data.

Example 54 includes the subject matter of any of examples 50-53, and wherein receiving vehicle assistance data includes receiving, by the in-vehicle computing system, refueling prediction data from the vehicle assistance server, and further comprising adjusting, by the in-vehicle computing system, a refueling estimate of the first vehicle based on the refueling prediction data, the refueling prediction data estimating a distance the first vehicle can travel before refueling is needed.

Example 55 includes the subject matter of any of examples 50-54, and wherein receiving vehicle assistance data comprises receiving, by the in-vehicle computing system from the vehicle assistance server, road condition data indicative of one or more road conditions for the first road segment, and further comprising generating, by the in-vehicle computing system, a notification about the one or more road conditions to inform a driver of the first vehicle.

Example 56 includes the subject matter of any of examples 50-55, and wherein receiving road condition data comprises receiving, by the in-vehicle computing system from the vehicle assistance server, road grade data indicative of a change in vertical elevation along the first road segment.

Example 57 includes one or more machine-readable storage media comprising a plurality of instructions stored thereon that in response to being executed result in a computing device performing the method of any of examples 29-56.

Example 58 includes a vehicle assistance server to assist a driver of a vehicle. The vehicle assistance server comprises means for receiving road data from each of a plurality of vehicles, wherein the road data comprises data indicative of at least one characteristic of a first road segment traversed by each of the plurality of vehicles; means for aggregating the road data received from each of the plurality of vehicles to generate crowd-sourced road data associated with the first road segment; means for receiving vehicle profile information from a first vehicle located on the first road segment, wherein the vehicle profile information defines at least one characteristic of the first vehicle; means for determining vehicle assistance data for the first vehicle based on the vehicle profile information and crowd-sourced road data associated with the first road segment, wherein the vehicle assistance data is usable by an in-vehicle computing device of the first vehicle to facilitate traversal of the first road segment by the vehicle; and means for transmitting the vehicle assistance data to the first vehicle.

Example 59 includes the subject matter of example 58, and further comprising means for determining road grade data for the first road segment based on the crowd-sourced road data, the road grade data indicating a change in vertical elevation along the first road segment.

Example 60 includes the subject matter of example 58 or 59, and wherein the means for determining vehicle assistance data includes means for determining refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data estimates a distance the first vehicle can travel before refueling is needed.

Example 61 includes the subject matter of any of examples 58-60, and wherein the means for determining the refueling prediction data includes means for determining refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data includes an estimated distance traveled until the first vehicle requires refueling based on the determined grade of the first road segment.

Example 62 includes the subject matter of any of examples 58-61, and wherein the refueling prediction data is usable by an on-board computing device of the first vehicle to adjust the estimated distance until refueling according to a past fuel performance of the first vehicle on the first segment.

Example 63 includes the subject matter of any of examples 58-62, and wherein the means for determining vehicle assistance data includes means for determining cruise control data based on the road grade data and the vehicle profile information, wherein the cruise control data is usable by the in-vehicle computing device to adjust a throttle of an engine of the first vehicle.

Example 64 includes the subject matter of any of examples 58-63, and wherein the means for determining the cruise control data includes means for determining cruise control data usable by the on-board computing device to adjust a throttle of the engine before the first vehicle begins traversing the first segment.

Example 65 includes the subject matter of any of examples 58-64, and wherein the means for determining the cruise control data includes means for determining the cruise control data based on the road grade data and information included in the vehicle profile information that indicates an operating characteristic of a second vehicle while traversing the first road segment, wherein the second vehicle has at least one permanent characteristic in common with the first vehicle.

Example 66 includes the subject matter of any of examples 58-65, and further comprising means for determining refueling prediction data based on the crowd-sourced road data and the vehicle profile information, the refueling prediction data estimating a distance the first vehicle can travel before refueling is required, wherein the vehicle profile information includes driver profile data identifying a driver of the first vehicle from a plurality of potential drivers and includes an indicator of a driving style of the identified driver.

Example 67 includes the subject matter of any of examples 58-66, and further comprising means for receiving infrastructure road data from each of a plurality of infrastructure sensors associated with the first road segment, wherein the infrastructure road data includes at least one characteristic indicative of the first road segment traversed by each of the plurality of vehicles.

Example 68 includes the subject matter of any of examples 58-67, and wherein the means for receiving vehicle profile information includes means for receiving vehicle identification data indicative of one or more permanent characteristics of the vehicle and vehicle operation data indicative of trip-specific characteristics of the vehicle.

Example 69 includes the subject matter of any of examples 58-68, and wherein the means for determining vehicle assistance data includes means for determining road surface data indicative of a characteristic of a surface of the first road segment.

Example 70 includes the subject matter of any of examples 58-69, and wherein the means for determining the road surface data includes means for determining road type data indicative of a road type of the first road segment from a digital navigation map.

Example 71 includes the subject matter of any of examples 58-70, and further comprising means for receiving meteorological data from one or more infrastructure sensors associated with the first road segment, wherein the means for determining the road surface data comprises means for determining road surface data based on the road type data and the meteorological data.

Example 72 includes the subject matter of any of examples 58-71, and further including means for determining road hazard data indicative of one or more hazardous conditions for the first road segment based on the crowd-sourced road data.

Example 73 includes the subject matter of any of examples 58-72, and wherein the means for receiving the road data includes means for receiving operational data from each of the plurality of vehicles traversing the first road segment, and the means for determining the road hazard data includes means for determining a location of one or more potholes located in the first road segment based on the crowd-sourced road data and the vehicle operational data.

Example 74 includes the subject matter of any of examples 58-73, and wherein the means for determining vehicle assistance data includes means for determining one or more vehicle control commands usable by the in-vehicle computing device to adjust one or more vehicle parameters of the first vehicle.

Example 75 includes the subject matter of any of examples 58-74, and wherein the means for determining vehicle assistance data includes means for determining one or more notifications for a driver of the first vehicle to be delivered by an output device of the first vehicle.

Example 76 includes the subject matter of any of examples 58-75, and further comprising means for sending one or more notifications to one or more second vehicles traveling on the first road segment, wherein the one or more notifications are to alert the one or more second vehicles of the road condition for the first road segment.

Example 77 includes the subject matter of any one of examples 58-76, and further comprising means for generating a probabilistic model based on the crowd-sourced road data, and wherein the means for determining the vehicle assistance data comprises means for determining vehicle assistance data based on the vehicle profile information and the probabilistic model.

Example 78 includes the subject matter of any one of examples 58-77, and further comprising means for applying a weighting factor to road data received from each of the plurality of vehicles based on a date the road data was collected by each of the plurality of vehicles.

Example 79 includes an in-vehicle computing system to assist a driver of a first vehicle. The in-vehicle computing system includes: means for transmitting, by an in-vehicle computing system of a first vehicle while the first vehicle is on a first road segment, vehicle profile information indicative of at least one characteristic of the first vehicle to a vehicle assistance server; means for receiving vehicle assistance data from the vehicle assistance server, wherein the vehicle assistance data is generated based on the vehicle profile information and crowd-sourced road data associated with the first road segment; means for determining at least one vehicle control command based on the received vehicle assistance data; and means for adjusting a vehicle parameter of the first vehicle based on the vehicle control command.

Example 80 includes the subject matter of example 79, and further comprising means for generating a notification for a driver of the first vehicle including information related to the vehicle assistance data in response to receiving the vehicle assistance data.

Example 81 includes the subject matter of example 79 or 80, and wherein the means for communicating vehicle profile information includes means for transmitting vehicle operation data indicative of operation of the first vehicle while the first vehicle traverses the first road segment; and means for transmitting the vehicle operation data and vehicle identification data indicative of at least one permanent characteristic of the first vehicle.

Example 82 includes the subject matter of any of examples 79-81, and wherein the means for receiving vehicle assistance data includes means for receiving cruise control data, and the means for adjusting the vehicle parameter includes means for adjusting a throttle of an engine of the first vehicle based on the cruise control data.

Example 83 includes the subject matter of any of examples 79-82, and wherein the means for receiving vehicle assistance data includes means for receiving refueling prediction data from the vehicle assistance server, and further includes means for adjusting a refueling estimate of the first vehicle based on the refueling prediction data that estimates a distance the first vehicle can travel before refueling is needed.

Example 84 includes the subject matter of any of examples 79-83, and wherein the means for receiving vehicle assistance data includes means for receiving road condition data from the vehicle assistance server indicating one or more road conditions for the first road segment, and further includes means for generating a notification about the one or more road conditions to inform a driver of the first vehicle.

Example 85 includes the subject matter of any one of examples 79-84, and wherein the means for receiving road condition data includes means for receiving road grade data from the vehicle assistance server indicative of vertical elevation changes along the first road segment.

Claims

1. A vehicle assistance server for assisting a driver of a vehicle, the vehicle assistance server comprising:

a data collection module to receive road data from each of a plurality of vehicles, wherein the road data includes data indicative of at least one characteristic of a first road segment traversed by each of the plurality of vehicles;

a data aggregation module to aggregate the road data received from each of the plurality of vehicles to generate crowd-sourced road data associated with the first road segment;

a vehicle information module to receive vehicle profile information from a first vehicle located on the first road segment, wherein the vehicle profile information defines at least one characteristic of the first vehicle; and

a vehicle assistance data determination module to determine vehicle assistance data for the first vehicle based on the vehicle profile information and the crowd-sourced road data associated with the first road segment, wherein the vehicle assistance data is usable by an in-vehicle computing device of the first vehicle to facilitate traversal of the first road segment by the vehicle.

2. The vehicle assistance server of claim 1, wherein the vehicle assistance data determination module is to determine road grade data for the first road segment based on the crowd-sourced road data, wherein the road grade data is indicative of a change in vertical elevation along the first road segment.

3. The vehicle assistance server of claim 2, wherein the vehicle assistance data determination module is to determine refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data estimates a distance the first vehicle can travel before refueling is required.

4. The vehicle assistance server of claim 2, wherein the vehicle assistance data determination module is to determine cruise control data based on the road grade data and the vehicle profile information, wherein the cruise control data is usable by the in-vehicle computing device to adjust a throttle of an engine of the first vehicle.

5. The vehicle assistance server of claim 1, wherein the vehicle information module receives vehicle identification data indicative of one or more permanent characteristics of the vehicle and vehicle operation data indicative of trip-specific characteristics of the vehicle.

6. The vehicle assistance server of claim 1, wherein the vehicle assistance data determination module is to determine road surface data indicative of a characteristic of a surface of the first road segment.

7. The vehicle assistance server of claim 1, wherein the vehicle assistance data determination module is to determine road hazard data indicative of one or more hazardous conditions for the first road segment based on the crowd-sourced road data.

8. The vehicle assistance server of claim 1, wherein the vehicle assistance data determination module is to determine one or more vehicle control commands usable by the in-vehicle computing device to adjust one or more vehicle parameters of the first vehicle.

9. A method for assisting a driver of a vehicle, the method comprising:

receiving, by a vehicle assistance server, road data from each of a plurality of vehicles, wherein the road data comprises data indicative of at least one characteristic of a first road segment traversed by each of the plurality of vehicles;

aggregating, by the vehicle assistance server, the road data received from each of the plurality of vehicles to generate crowd-sourced road data associated with the first road segment;

receiving, by the vehicle assistance server, vehicle profile information from a first vehicle located on the first segment, wherein the vehicle profile information defines at least one characteristic of the first vehicle;

determining, by the vehicle assistance server, vehicle assistance data for the first vehicle based on the vehicle profile information and the crowd-sourced road data associated with the first road segment, wherein the vehicle assistance data is usable by an in-vehicle computing device of the first vehicle to facilitate traversal of the first road segment by the vehicle; and

transmitting, by the vehicle assistance server, the vehicle assistance data to the first vehicle.

10. The method of claim 9, further comprising: determining, by the vehicle assistance server, road grade data for the first road segment based on the crowd-sourced road data, the road grade data indicating a vertical elevation change along the first road segment.

11. The method of claim 10, wherein determining vehicle assistance data comprises: determining, by the vehicle assistance server, refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data estimates a distance that the first vehicle can travel before refueling is required.

12. The method of claim 10, wherein determining vehicle assistance data comprises: determining, by the vehicle assistance server, cruise control data based on the road grade data and the vehicle profile information, wherein the cruise control data is usable by the in-vehicle computing device to adjust a throttle of an engine of the first vehicle.

13. The method of claim 9, wherein determining vehicle assistance data comprises: determining, by the vehicle assistance server, road surface data indicative of a characteristic of a surface of the first road segment.

14. A vehicle assistance server for assisting a driver of a vehicle, the vehicle assistance server comprising:

means for receiving road data from each of a plurality of vehicles, wherein the road data comprises data indicative of at least one characteristic of a first road segment traversed by each of the plurality of vehicles;

means for aggregating the road data received from each of the plurality of vehicles to generate crowd-sourced road data associated with the first road segment;

means for receiving vehicle profile information from a first vehicle located on the first road segment, wherein the vehicle profile information defines at least one characteristic of the first vehicle;

means for determining vehicle assistance data for the first vehicle based on the vehicle profile information and the crowd-sourced road data associated with the first road segment, wherein the vehicle assistance data is usable by an in-vehicle computing device of the first vehicle to facilitate traversal of the first road segment by the vehicle; and

means for transmitting the vehicle assistance data to the first vehicle.

15. The vehicle assistance server of claim 14, further comprising: means for determining road grade data for the first road segment based on the crowd-sourced road data, the road grade data indicating a change in vertical elevation along the first road segment.

16. The vehicle assistance server of claim 15, wherein the means for determining comprises: means for determining refueling prediction data based on the road grade data and the vehicle profile information, wherein the refueling prediction data estimates a distance that the first vehicle can travel before refueling is required.

17. The vehicle assistance server of claim 15, wherein the means for determining vehicle assistance data comprises: means for determining cruise control data based on the road grade data and the vehicle profile information, wherein the cruise control data is usable by the in-vehicle computing device to adjust a throttle of an engine of the first vehicle.

18. The vehicle assistance server of claim 14, wherein the means for receiving vehicle profile information comprises: means for receiving vehicle identification data indicative of one or more permanent characteristics of the vehicle and vehicle operation data indicative of trip-specific characteristics of the vehicle.

19. The vehicle assistance server of claim 14, wherein the means for determining vehicle assistance data comprises: means for determining road surface data indicative of a characteristic of a surface of the first road segment.

20. The vehicle assistance server of claim 14, further comprising: means for determining road hazard data indicative of one or more hazardous conditions for the first road segment based on the crowd-sourced road data.

21. The vehicle assistance server of claim 14, wherein the means for determining vehicle assistance data comprises: means for determining one or more vehicle control commands usable by the in-vehicle computing device to adjust one or more vehicle parameters of the first vehicle.

22. An in-vehicle computing system for assisting a driver of a first vehicle, the in-vehicle computing system comprising:

a vehicle profile module to transmit vehicle profile information of the first vehicle indicative of at least one characteristic of the first vehicle to a vehicle assistance server while the first vehicle is on a first road segment;

a vehicle output module to: (i) receive vehicle assistance data from the vehicle assistance server, wherein the vehicle assistance data is generated based on the vehicle profile information and crowd-sourced road data associated with the first road segment; (ii) determining the vehicle output module to determine at least one vehicle control command based on the received vehicle assistance data; and (iii) adjusting a vehicle parameter of the first vehicle based on the vehicle control command.

23. The in-vehicle computing system of claim 22, wherein the vehicle output module is to:

receives cruise control data, and

adjusting the vehicle parameter comprises: adjusting, by the on-board computing system, a throttle of an engine of the first vehicle based on the cruise control data.

24. The in-vehicle computing system of claim 22, wherein the vehicle output module is to:

receiving refueling prediction data that estimates a distance that the first vehicle can travel before refueling is needed, an

Adjusting a throttle of an engine of the first vehicle based on the cruise control data.

25. The in-vehicle computing system of claim 22, wherein the vehicle output module is to:

receiving, from the vehicle assistance server, road condition data indicative of one or more road conditions for the first road segment; and

generating a notification regarding the one or more road conditions to inform a driver.

26. One or more machine-readable storage media comprising a plurality of instructions stored thereon that in response to being executed result in a computing device performing the method of any of claims 9-13.